In this work, a state-of-the-art bed-side monitor combining time-domain near infrared spectroscopy, diffuse correlation spectroscopy, a pulse oximeter, and an automatized tourniquet was utilized. An automatized vascular occlusion test was used to evaluate peripheral endothelial, microvascular, and metabolic function in a subject admitted to the intensive care (ICU). The data used in this study are read from binary files produced by the platform. These binary files follow a pre-determined format, allowing them to be easily read and processed using various platforms such as MATLAB, Python, and R. The data processing involves then importing the data from these files, followed by appropriate preprocessing steps to ensure appropriate data quality of the displayed data. Preprocessing involves several key steps to prepare the data for analysis. First, movement artifacts are removed. Next, data alignment is performed to synchronize time-series data. We utilize the real time data extracted from the binary data to extract relevant features: simple linear fitting and integration techniques are used to retrieve the deoxygenation and reoxygenation rates during the VOT, along with the area under the curves for tissue oxygenation and blood flow, respectively.

Matlab, 2021b